Inspired by CoAD [1]}, we jointly optimize the co-saliency and single image saliency predictions in a multi-task learning framework. Similar to BASNet [2]}, we use pixel-level, region-level and object-level supervision strategy to better keep the uniformity and wholeness of the co-salient objects. Specifically, binary cross-entropy (BCE) [3]}, SSIM [4]} and F-measure (\(F_m\) ) loss [5]} are denoted as pixel-level, region-level and object-level loss.
we supervise the predicted co-saliency maps \(\mathcal {M}=\lbrace M^{(n)}\rbrace _{n=1}^N\)  by the corresponding groundtruth \(\mathcal {T}=\lbrace {T}^{(n)}\rbrace _{n=1}^N\)  under these three loss:
\(L_c = BCE(\mathcal {M},\mathcal {T}) + SSIM(\mathcal {M},\mathcal {T}) + F_m(\mathcal {M},\mathcal {T}).\) 
